{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb425bf5",
   "metadata": {},
   "source": [
    "### Image Question with Qwen3-Omni"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b92d046f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['VLLM_USE_V1'] = '0'\n",
    "os.environ['VLLM_WORKER_MULTIPROC_METHOD'] = 'spawn'\n",
    "os.environ[\"VLLM_LOGGING_LEVEL\"] = \"ERROR\"\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = \"0\"\n",
    "import torch\n",
    "import warnings\n",
    "import numpy as np\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.filterwarnings('ignore', category=DeprecationWarning)\n",
    "warnings.filterwarnings('ignore', category=FutureWarning)\n",
    "warnings.filterwarnings('ignore', category=UserWarning)\n",
    "\n",
    "from qwen_omni_utils import process_mm_info\n",
    "from transformers import Qwen3OmniMoeProcessor\n",
    "\n",
    "def _load_model_processor():\n",
    "    if USE_TRANSFORMERS:\n",
    "        from transformers import Qwen3OmniMoeForConditionalGeneration\n",
    "        if TRANSFORMERS_USE_FLASH_ATTN2:\n",
    "            model = Qwen3OmniMoeForConditionalGeneration.from_pretrained(MODEL_PATH,\n",
    "                                                                         dtype='auto',\n",
    "                                                                         attn_implementation='flash_attention_2',\n",
    "                                                                         device_map=\"auto\")\n",
    "        else:\n",
    "            model = Qwen3OmniMoeForConditionalGeneration.from_pretrained(MODEL_PATH, device_map=\"auto\", dtype='auto')\n",
    "    else:\n",
    "        from vllm import LLM\n",
    "        model = LLM(\n",
    "            model=MODEL_PATH, trust_remote_code=True, gpu_memory_utilization=0.95,\n",
    "            tensor_parallel_size=torch.cuda.device_count(),\n",
    "            limit_mm_per_prompt={'image': 1, 'video': 3, 'audio': 3},\n",
    "            max_num_seqs=1,\n",
    "            max_model_len=32768,\n",
    "            seed=1234,\n",
    "        )\n",
    "\n",
    "    processor = Qwen3OmniMoeProcessor.from_pretrained(MODEL_PATH)\n",
    "    return model, processor\n",
    "\n",
    "def run_model(model, processor, messages, return_audio, use_audio_in_video):\n",
    "    if USE_TRANSFORMERS:\n",
    "        text = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)\n",
    "        audios, images, videos = process_mm_info(messages, use_audio_in_video=use_audio_in_video)\n",
    "        inputs = processor(text=text, audio=audios, images=images, videos=videos, return_tensors=\"pt\", padding=True, use_audio_in_video=use_audio_in_video)\n",
    "        inputs = inputs.to(model.device).to(model.dtype)\n",
    "        text_ids, audio = model.generate(**inputs, \n",
    "                                            thinker_return_dict_in_generate=True,\n",
    "                                            thinker_max_new_tokens=8192, \n",
    "                                            thinker_do_sample=False,\n",
    "                                            speaker=\"Ethan\", \n",
    "                                            use_audio_in_video=use_audio_in_video,\n",
    "                                            return_audio=return_audio)\n",
    "        response = processor.batch_decode(text_ids.sequences[:, inputs[\"input_ids\"].shape[1] :], skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]\n",
    "        if audio is not None:\n",
    "            audio = np.array(audio.reshape(-1).detach().cpu().numpy() * 32767).astype(np.int16)\n",
    "        return response, audio\n",
    "    else:\n",
    "        from vllm import SamplingParams\n",
    "        sampling_params = SamplingParams(temperature=1e-2, top_p=0.1, top_k=1, max_tokens=8192)\n",
    "        text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
    "        audios, images, videos = process_mm_info(messages, use_audio_in_video=use_audio_in_video)\n",
    "        inputs = {'prompt': text, 'multi_modal_data': {}, \"mm_processor_kwargs\": {\"use_audio_in_video\": use_audio_in_video}}\n",
    "        if images is not None: inputs['multi_modal_data']['image'] = images\n",
    "        if videos is not None: inputs['multi_modal_data']['video'] = videos\n",
    "        if audios is not None: inputs['multi_modal_data']['audio'] = audios\n",
    "        outputs = model.generate(inputs, sampling_params=sampling_params)\n",
    "        response = outputs[0].outputs[0].text\n",
    "        return response, None\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d37dcedc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Unrecognized keys in `rope_scaling` for 'rope_type'='default': {'interleaved', 'mrope_section'}\n",
      "Unrecognized keys in `rope_scaling` for 'rope_type'='default': {'interleaved', 'mrope_section'}\n",
      "`torch_dtype` is deprecated! Use `dtype` instead!\n",
      "2025-09-14 20:32:11,498\tINFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n",
      "You are attempting to use Flash Attention 2 without specifying a torch dtype. This might lead to unexpected behaviour\n",
      "Loading safetensors checkpoint shards:   0% Completed | 0/15 [00:00<?, ?it/s]\n",
      "Loading safetensors checkpoint shards:   7% Completed | 1/15 [00:00<00:09,  1.52it/s]\n",
      "Loading safetensors checkpoint shards:  20% Completed | 3/15 [00:01<00:06,  1.78it/s]\n",
      "Loading safetensors checkpoint shards:  27% Completed | 4/15 [00:03<00:09,  1.20it/s]\n",
      "Loading safetensors checkpoint shards:  33% Completed | 5/15 [00:03<00:06,  1.65it/s]\n",
      "Loading safetensors checkpoint shards:  40% Completed | 6/15 [00:04<00:07,  1.17it/s]\n",
      "Loading safetensors checkpoint shards:  47% Completed | 7/15 [00:05<00:08,  1.03s/it]\n",
      "Loading safetensors checkpoint shards:  53% Completed | 8/15 [00:07<00:07,  1.12s/it]\n",
      "Loading safetensors checkpoint shards:  60% Completed | 9/15 [00:08<00:07,  1.18s/it]\n",
      "Loading safetensors checkpoint shards:  67% Completed | 10/15 [00:10<00:06,  1.26s/it]\n",
      "Loading safetensors checkpoint shards:  73% Completed | 11/15 [00:11<00:05,  1.30s/it]\n",
      "Loading safetensors checkpoint shards:  80% Completed | 12/15 [00:12<00:03,  1.25s/it]\n",
      "Loading safetensors checkpoint shards:  87% Completed | 13/15 [00:13<00:02,  1.28s/it]\n",
      "Loading safetensors checkpoint shards:  93% Completed | 14/15 [00:15<00:01,  1.32s/it]\n",
      "Loading safetensors checkpoint shards: 100% Completed | 15/15 [00:16<00:00,  1.33s/it]\n",
      "Loading safetensors checkpoint shards: 100% Completed | 15/15 [00:16<00:00,  1.12s/it]\n",
      "\n",
      "The image processor of type `Qwen2VLImageProcessor` is now loaded as a fast processor by default, even if the model checkpoint was saved with a slow processor. This is a breaking change and may produce slightly different outputs. To continue using the slow processor, instantiate this class with `use_fast=False`. Note that this behavior will be extended to all models in a future release.\n",
      "Unused or unrecognized kwargs: images.\n",
      "Capturing CUDA graph shapes: 100%|██████████| 1/1 [00:00<00:00,  1.82it/s]\n"
     ]
    }
   ],
   "source": [
    "import librosa\n",
    "import audioread\n",
    "\n",
    "from IPython.display import Video\n",
    "from IPython.display import Image\n",
    "from IPython.display import Audio\n",
    "\n",
    "MODEL_PATH = \"Qwen/Qwen3-Omni-30B-A3B-Instruct\"\n",
    "# MODEL_PATH = \"Qwen/Qwen3-Omni-30B-A3B-Thinking\"\n",
    "\n",
    "USE_TRANSFORMERS = False\n",
    "TRANSFORMERS_USE_FLASH_ATTN2 = True\n",
    "\n",
    "model, processor = _load_model_processor()\n",
    "\n",
    "USE_AUDIO_IN_VIDEO = True\n",
    "RETURN_AUDIO = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0b1330dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "height": 360,
       "width": 640
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Adding requests: 100%|██████████| 1/1 [00:05<00:00,  5.44s/it]\n",
      "Processed prompts: 100%|██████████| 1/1 [00:02<00:00,  2.88s/it, est. speed input: 72.69 toks/s, output: 113.04 toks/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Based on the visual characteristics of the image, this artwork is a prime example of **Surrealism**.\n",
      "\n",
      "Here are the key elements that point to this style:\n",
      "\n",
      "*   **Dreamlike and Illogical Imagery:** The central figure is a bizarre, non-realistic creature. It appears to be a combination of a human head, a large red tower or column, and a spinning top. This impossible fusion of disparate objects is a hallmark of Surrealism.\n",
      "*   **Juxtaposition of Unrelated Objects:** The artist places a human-like figure with a tower for a body and a spinning top for a base in a barren, desert landscape. This creates a strange and unsettling scene that defies the laws of nature and logic.\n",
      "*   **Symbolism:** The image is rich with symbolic content. The tower could represent a fortress, a prison, or a monument to the past. The spinning top suggests instability, the passage of time, or a precarious existence. The figure's hands are raised in a gesture that could be interpreted as prayer, surrender, or despair. The overall mood is one of existential anxiety and isolation.\n",
      "*   **Atmosphere:** The vast, empty desert and the dramatic, cloudy sky create a powerful and melancholic atmosphere, focusing all attention on the strange central figure.\n",
      "\n",
      "This specific painting is **\"The Enigma of Desire\" (L'Énigme du désir)** by the Spanish artist **Salvador Dalí**, created in 1929. It is considered one of his most important early works and a foundational piece of the Surrealist movement.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "image_path = \"https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-Omni/cookbook/2621.jpg\"\n",
    "\n",
    "messages = [\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [\n",
    "            {\"type\": \"image\", \"image\": image_path},\n",
    "            {\"type\": \"text\", \"text\": \"What style does this image depict?\"},\n",
    "        ]\n",
    "    }\n",
    "]\n",
    "\n",
    "display(Image(image_path, width=640, height=360))\n",
    "\n",
    "response, audio = run_model(model=model, messages=messages, processor=processor, return_audio=RETURN_AUDIO, use_audio_in_video=USE_AUDIO_IN_VIDEO)\n",
    "\n",
    "print(response)\n",
    "if audio is not None:\n",
    "    display(Audio(audio, rate=24000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "61c7f53d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "height": 360,
       "width": 640
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Adding requests: 100%|██████████| 1/1 [00:00<00:00, 103.81it/s]\n",
      "Processed prompts: 100%|██████████| 1/1 [00:02<00:00,  2.69s/it, est. speed input: 67.97 toks/s, output: 119.23 toks/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Based on the visual cues in the image, the most likely event to happen next is a storm.\n",
      "\n",
      "Here is a breakdown of the reasoning:\n",
      "\n",
      "1.  **The Sky:** The sky is filled with dark, heavy, and turbulent clouds. This type of cloud formation is characteristic of a cumulonimbus or a dense stratocumulus layer, both of which are associated with storms. The ominous, low-hanging clouds suggest that precipitation is imminent.\n",
      "\n",
      "2.  **The Field:** The field is a lush, green crop, likely wheat or barley, that appears healthy and ready for harvest. This type of vegetation is highly susceptible to damage from strong winds and heavy rain.\n",
      "\n",
      "3.  **The Atmosphere:** The overall lighting is dim and gray, indicating that the sun is completely obscured by the thick cloud cover. This lack of direct sunlight is typical of the period just before a storm hits.\n",
      "\n",
      "Given these elements, the most probable sequence of events is that the storm will arrive, bringing with it heavy rain and strong winds. This could lead to several outcomes for the field, such as:\n",
      "\n",
      "*   **Heavy Rainfall:** The crops could be soaked, and the soil could become waterlogged.\n",
      "*   **Strong Winds:** The wind could bend or break the stalks of the crops, causing significant damage to the yield.\n",
      "*   **Hail:** If the storm is severe, it could bring hail, which would physically damage the grain heads.\n",
      "\n",
      "In summary, the image captures the tense moment just before a storm breaks, and the most likely next event is the onset of this storm.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "image_path = \"https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-Omni/cookbook/2233.jpg\"\n",
    "\n",
    "messages = [\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [\n",
    "            {\"type\": \"image\", \"image\": image_path},\n",
    "            {\"type\": \"text\", \"text\": \"Based on this image, what do you think will happen next?\"},\n",
    "        ]\n",
    "    }\n",
    "]\n",
    "\n",
    "display(Image(image_path, width=640, height=360))\n",
    "\n",
    "response, audio = run_model(model=model, messages=messages, processor=processor, return_audio=RETURN_AUDIO, use_audio_in_video=USE_AUDIO_IN_VIDEO)\n",
    "\n",
    "print(response)\n",
    "if audio is not None:\n",
    "    display(Audio(audio, rate=24000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c5806c45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "height": 360,
       "width": 640
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Adding requests: 100%|██████████| 1/1 [00:00<00:00, 241.89it/s]\n",
      "Processed prompts: 100%|██████████| 1/1 [00:00<00:00,  9.17it/s, est. speed input: 1351.22 toks/s, output: 18.51 toks/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "image_path = \"https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-Omni/cookbook/val_IQ_Test_113.jpg\"\n",
    "\n",
    "messages = [\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [\n",
    "            {\"type\": \"image\", \"image\": image_path},\n",
    "            {\"type\": \"text\", \"text\": \"Identify the one picture that follows the same pattern or rule established by the previous pictures. Please directly the correct answer from the options above.\"},\n",
    "        ]\n",
    "    }\n",
    "]\n",
    "\n",
    "display(Image(image_path, width=640, height=360))\n",
    "\n",
    "response, audio = run_model(model=model, messages=messages, processor=processor, return_audio=RETURN_AUDIO, use_audio_in_video=USE_AUDIO_IN_VIDEO)\n",
    "\n",
    "print(response)\n",
    "if audio is not None:\n",
    "    display(Audio(audio, rate=24000))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "qwen3_omni_public",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
